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Ecological Inference P1: FZZ/FZZ P2: FZZ CB658-FMDVR CB654-KING-Sample CB658-KING-Sample.cls June 25, 2004 6:14 Ecological Inference Ecological Inference: New Methodological Strategies brings together a diverse group of scholars to survey the latest strategies for solving ecological inference problems in various fields. The last half decade has witnessed an explosion of research in ecological inference – the attempt to infer individual behavior from aggregate data. The uncertainties and the information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but such inferences are required in many academic fields, as well as by legislatures and the courts in redistricting, by businesses in marketing research, and by governments in policy analysis. Gary King is the David Florence Professor of Government at Harvard University. He also serves as the director of the Harvard–MIT Data Center and as a member of the steering committee of Harvard’s Center for Basic Research in the Social Sciences. He was elected president of the Society for Political Methodology and Fellow of the American Academy of Arts and Sciences. Professor King received his Ph.D. from the University of Wisconsin. He has won numerous awards for his work, including the Gosnell Prize for his book A Solution to the Ecological Inference Problem (1997), on which the research in this book builds. His home page can be found at http:// GKing.Harvard.edu. Ori Rosen is Assistant Professor of Statistics at the University of Pittsburgh. His research includes work on semiparametric regression models, applications of mixtures-of-experts neural network models in regression, and applications of Markov chain Monte Carlo methods. Professor Rosen was educated at the Tech- nion, and he later served as Mellon Postdoctoral Fellow at Northwestern University. Martin A. Tanner is Professor of Statistics at Northwestern University. He has au- thored and coauthored nearly 100 research articles on wide-ranging topics in theo- retical and applied statistics. His previous books include Investigations for a Course in Statistics (1990), Tools for Statistical Inference (1996), and Statistics for the 21st Century (2001). Professor Tanner is a Fellow of the American Statistical Association and the Royal Statistical Society, and he has been honored with the 1993 Mortimer Spiegelman Award as well as the American Statistical Association’s Continuing Ed- ucation Excellence Award. He has served as editor of the Journal of the American Statistical Association (Theory and Methods). Professor Tanner received his Ph.D. from the University of Chicago. i P1: FZZ/FZZ P2: FZZ CB658-FMDVR CB654-KING-Sample CB658-KING-Sample.cls June 25, 2004 6:14 Analytical Methods for Social Research Series Editors R. Michael Alvarez, California Institute of Technology Nathaniel L. Beck, New York University Lawrence L. Wu, New York University Analytical Methods for Social Research presents texts on empirical and formal methods for the social sciences. Some series volumes are broad in scope, addressing multiple disciplines; others focus mainly on techniques applied within specific fields, such as political science, sociology, and demography. Previously published: Event History Modeling: A Guide for Social Scientists, Janet M. Box-Steffensmeier and BradfordS.Jones ii P1: FZZ/FZZ P2: FZZ CB658-FMDVR CB654-KING-Sample CB658-KING-Sample.cls June 25, 2004 6:14 Ecological Inference New Methodological Strategies Edited by Gary King Harvard University Ori Rosen University of Pittsburgh Martin A. Tanner Northwestern University iii P1: FZZ/FZZ P2: FZZ CB658-FMDVR CB654-KING-Sample CB658-KING-Sample.cls June 25, 2004 6:14 PUBLISHED BY THE PRESS SYNDICATE OF THE UNIVERSITY OF CAMBRIDGE The Pitt Building, Trumpington Street, Cambridge, United Kingdom CAMBRIDGE UNIVERSITY PRESS The Edinburgh Building, Cambridge CB2 2RU, UK 40 West 20th Street, New York, NY 10011-4211, USA 477 Williamstown Road, Port Melbourne, VIC 3207, Australia Ruiz de Alarcon´ 13, 28014 Madrid, Spain Dock House, The Waterfront, Cape Town 8001, South Africa http://www.cambridge.org C Cambridge University Press 2004 This book is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published 2004 Printed in the United States of America Typefaces Minion 10/12 pt., Helvetica Neue Condensed, and Lucida Typewriter System LATEX2ε [TB] A catalog record for this book is available from the British Library. Library of Congress Cataloging in Publication Data Ecological inference : new methodological strategies / edited by Gary King, Matrin A. Tanner, Ori Rosen. p. cm. Includes bibliographical references (p. ). ISBN 0-521-83513-5 – ISBN 0-521-54280-4 (pbk.) 1. Social sciences – Statistical methods. 2. Political statistics. 3. Inference. I. King, Gary. II. Tanner, Martin Abba, 1957– III. Rosen, Ori. HA29.E27 2004 330.727 – dc22 2004045500 ISBN 0 521 83513 5 hardback ISBN 0 521 54280 4 paperback iv P1: FZZ/FZZ P2: FZZ CB658-FMDVR CB654-KING-Sample CB658-KING-Sample.cls June 25, 2004 6:14 Contents Contributors page vii Preface ix INTRODUCTION 1 Information in Ecological Inference: An Introduction 1 Gary King, Ori Rosen, and Martin A. Tanner PART ONE 13 1 Prior and Likelihood Choices in the Analysis of Ecological Data 13 Jonathan Wakefield 2 The Information in Aggregate Data 51 David G. Steel, Eric J. Beh, and Ray L. Chambers 3 Using Ecological Inference for Contextual Research 69 D. Stephen Voss PART TWO 97 4 Extending King’s Ecological Inference Model to Multiple Elections Using Markov Chain Monte Carlo 97 Jeffrey B. Lewis 5 Ecological Regression and Ecological Inference 123 Bernard Grofman and Samuel Merrill 6 Using Prior Information to Aid Ecological Inference: A Bayesian Approach 144 J. Kevin Corder and Christina Wolbrecht 7 An Information Theoretic Approach to Ecological Estimation and Inference 162 George G. Judge, Douglas J. Miller, and Wendy K. Tam Cho 8 Ecological Panel Inference from Repeated Cross Sections 188 Ben Pelzer, Rob Eisinga, and Philip Hans Franses PART THREE 207 9 Ecological Inference in the Presence of Temporal Dependence 207 Kevin M. Quinn 10 A Spatial View of the Ecological Inference Problem 233 Carol A. Gotway Crawford and Linda J. Young v P1: FZZ/FZZ P2: FZZ CB658-FMDVR CB654-KING-Sample CB658-KING-Sample.cls June 25, 2004 6:14 vi Contents 11 Places and Relationships in Ecological Inference 245 Ernesto Calvo and Marcelo Escolar 12 Ecological Inference Incorporating Spatial Dependence 266 Sebastien Haneuse and Jonathan Wakefield PART FOUR 303 13 Common Framework for Ecological Inference in Epidemiology, Political Science, and Sociology 303 Ruth Salway and Jonathan Wakefield 14 MultipartySplit-TicketVotingEstimationasanEcologicalInferenceProblem 333 Kenneth Benoit, Michael Laver, and Daniela Giannetti 15 A Structured Comparison of the Goodman Regression, the Truncated Normal, and the Binomial–Beta Hierarchical Methods for Ecological Inference 351 Rogerio´ Silva de Mattos and Alvaro´ Veiga 16 A Comparison of the Numerical Properties of EI Methods 383 Micah Altman, Jeff Gill, and Michael P. McDonald Index 409 P1: FZZ/FZZ P2: FZZ CB658-FMDVR CB654-KING-Sample CB658-KING-Sample.cls June 25, 2004 6:14 Contributors Micah Altman Harvard-MIT Data Center (HMDC), Harvard University, Cambridge, Massachusetts Eric J. Beh School of Quantitative Methods and Mathematical Sciences, University of Western Sydney, Sydney, Australia Kenneth Benoit Department of Political Science, Trinity College, Dublin, Ireland Ernesto Calvo Department of Political Science, University of Houston, Houston, Texas Ray L. Chambers Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, United Kingdom Wendy K. Tam Cho Departments of Political Science and Statistics, University of Illinois at Urbana-Champaign, Urbana, Illinois J.KevinCorder DepartmentofPoliticalScience,WesternMichiganUniversity,Kalamazoo, Michigan Carol A. Gotway Crawford National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia Rob Eisinga Department of Social Science Research Methods, University of Nijmegen, Nijmegen, The Netherlands Marcelo Escolar Department of Geography, Universidad de Buenos Aires, Buenos Aires, Argentina Philip Hans Franses Econometric Institute, Erasmus University, Rotterdam, The Nether- lands Daniela Giannetti Department of Political Science, University of Bologna, Bologna, Italy Jeff Gill Department of Political Science, University of California, Davis, California Bernard Grofman Department of Political Science, University of California, Irvine, Cali- fornia Sebastien Haneuse Department of Biostatistics, University of Washington, Seattle, Washington George G. Judge Department of Agricultural and Resource Economics, University of Cal- ifornia, Berkeley, California vii P1: FZZ/FZZ P2: FZZ CB658-FMDVR CB654-KING-Sample CB658-KING-Sample.cls June 25, 2004 6:14 viii Contributors Gary King Center for Basic Research in the Social Sciences, Harvard University, Cam- bridge, Massachusetts Michael Laver Department of Political Science, Trinity College, Dublin, Ireland Jeffrey B. Lewis Department of Political Science, University of California, Los Angeles, California Rogerio´ Silva de Mattos Department of Economic Analysis, Federal University of Juiz de Fora, Minas Gerais, Brazil Michael P. McDonald Department of Public & International Affairs, George
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